Computer Networks Research Articles

From building blocks to digital networks: analyzing critical drivers for social media adoption in the construction sector

Despite the growing recognition of social media’s potential benefits in the construction industry, there is a lack of in-depth research examining the specific drivers that influence its adoption. Therefore, this study fills this gap by empirically investigating the drivers influencing the adoption of social media in the construction sector of a developing country such as Nigeria. This objective was achieved through a quantitative research approach, involving the utilization of a well-structured questionnaire to gather responses from construction professionals, including architects, builders, engineers, quantity surveyors and estate managers. These professionals were sampled using a combination of purposive and snowball sampling techniques. The obtained data underwent analysis using various statistical methods, including descriptive statistics and inferential statistics such as exploratory factor analysis (EFA) and Horn Parallel Analysis. The EFA revealed four clusters of drivers: communication and collaboration, operational efficiency and risk management, marketing and branding and knowledge sharing and learning. The findings of this study offer valuable guidance for construction firms and professionals seeking to harness the power of social media. Understanding the specific drivers within each cluster can inform strategic decisions regarding the selection and implementation of social media tools.

India’s Framework for Cyber Security since 2014 and its Significance for Armed Forces

The character of warfare changes with the change in its nature. In the last two decades, cyberattacks have expanded in their dimensions with the advent of digital networking in the military sector. Cyber security occupies a significant position in the Indian armed forces because of the increasing threat of cyberattacks from hostile neighbouring countries, particularly China and Pakistan. Cyber threat undermines military preparedness and warfighting capabilities by infiltrating critical weapon systems, vital infrastructure, crucial communication networks, and Intelligence, Surveillance, and reconnaissance (ISR) systems. India is now concentrating on framing its cyber security policy to mitigate the cyber threat and enhance the cyber capabilities of its armed forces. With its cyber security policy, India aspires to make digital armed forces. The present research article will focus on the efforts the Indian government has taken since 2014 to neutralize cyberattacks and the policies it has framed to ensure cyber security. The article will also analyze the significance of the government efforts on the armed forces in the direction of defensive cyber policy.

LPCE-Based Replacement Scheme for Enhancing Caching Performance in Named Data Networking

The substantial surge in users has adversely impacted the performance of the present IP-based Internet. Named data networking (NDN) emerges as a future alternative, given its distributed content caching system, where data can be cached in multiple routers and retrieved from the closest one instead of the original producer, enhancing content availability, reducing latency, and minimizing data loss. This paper introduces the less popular content eviction (LPCE) policy, a novel cache replacement scheme designed to enhance the caching performance of the conventional LFU (least frequently used) policy in NDN routers, thereby improving overall network efficiency. The proposed method subsumes LFU and FIFO (first in first out) policies and employs an additional list controlled by the LRU (least recently used) policy. Utilizing the ccnSim simulator, we conduct a comparison of LPCE’s performance with that of the LFU technique and other competing caching techniques, considering variations in several simulation parameters. Experimental results reveal that the proposed LPCE algorithm excels over others across a majority of performance metrics, such as cache hit ratio, content delivery delay, upstream hop count, network traffic, and producers’ load. Besides, the findings indicate that LPCE outperforms LFU, with an increase in cache hit ratio ranging from 1.32% to 5.75%.

INFRASTRUKTUR JARINGAN KOMPUTER BERBASIS CISCO PACKET TRACER DI LABKOM E-18 UNIVERSITAS MUHAMMADIYAH SUKABUMI

Computer network technology plays a crucial role in supporting teaching and learning activities. A Local Area Network (LAN) is a type of computer network that connects devices within a specific area. The application of network technology supports the learning process, including in the Computer Laboratory of the Information Technology Education Program. This research aims to design and configure the computer network infrastructure in the laboratory using wired media. The method used is the Network Development Life Cycle (NDLC), which relies on previous development processes. The research methodology involves simulation with Cisco Packet Tracer to design network topology and configure devices. The research results include the design of computer network infrastructure using wired media and optimal IP configuration. Findings indicate that the devices within the designed network topology are interconnected and can access the internet effectively.

READ: Resource efficient authentication scheme for digital twin edge networks

In recent vigorous developments, digital twin edge networks (DITEN) have emerged as a network paradigm to improve network communication efficiency. Given that Web 3.0 technologies promise secure decentralized data storage and effective information exchange, it is feasible to construct a wireless edge intelligence-enabled Web 3.0 physical infrastructure through DITEN. However, DITEN encounters various security threats related to communication and authentication, and establishing a secure and cost-effective authentication scheme for confidential access to physical entities poses a significant challenge. To tackle this issue, in this article, we introduce READ, a provably secure multi-factor user authentication scheme tailored for DITEN in industrial applications. Using designed ASCON cryptography primitive cipher suite, physical unclonable functions, extended Chebyshev chaotic maps, one-way secure collision-resistant hash functions, and lightweight bitwise exclusive-or operations, READ enables mutual authentication and session key negotiation among mobile users, smart gateways, and smart industrial devices. Rigorous security assessments, conducted through the real-or-random (ROR) model, the automated validation of internet security-sensitive protocols and applications (AVISPA) simulation tool, and heuristic informal security analysis, confirm that READ meets all 13 security evaluation criteria. Furthermore, compared to other seven advanced multi-factor user authentication schemes, READ excels in security and efficiency, making it ideal for practical multi-factor user authentication scenarios.

Manifestations of gender stereotyped patterns in activities in digital environments of Chilean university students

Introduction: gender stereotypes are transmitted from generation to generation and become naturalized, making it difficult to deconstruct gender roles. Digital environments are not free of these stereotypical patterns, perpetuating values ​​and social roles related to gender. The objective of this research is to identify activities in digital environments of Chilean university students that may be associated with gender stereotypical behaviors.Methods: 324 women and 185 men aged 18 to 27 were evaluated through a questionnaire with a six-level Likert scale, where information was collected on the frequency with which students provided information and carried out actions on networks. Significant differences between sexes were analyzed using Mann-Whitney U.Results: women are more likely to provide personal information when requested. Men are more likely to use aliases, include avatar photos, and accept strangers into their networks. Women update their profile picture more and check who sees their posts. Men tend to make public more sensitive personal information such as political tendencies and religious beliefs.Conclusions: college students engage in stereotypical gender behavior on digital networks. These stereotypical behaviors condition students' behavior on social networks, leading to a migration from the analog to the digital world in terms of types of behavior, perpetuating social gender roles.

Sorting Permutations on an n − Broom

With applications in computer networks, robotics, genetics, data center network optimization, cryptocurrency exchange, transportation and logistics, cloud computing, and social network analysis, the problem of sorting permutations on transposition trees under various operations is highly relevant. The goal of the problem is to sort or rearrange the markers in a predetermined order by swapping them out at the vertices of a tree in the fewest possible swaps. Only certain classes of transposition trees, like path, star, and broom, have computationally efficient algorithms for sorting permutations. In this paper, we examine the so-called n−broom transposition trees. A single broom or simply a broom is a spanning tree formed by joining the center of the star graph with one end of the path graph. A generalized version of a broom known as an n−broom is created by joining the ends of n brooms to one vertex, known as the n−broom center. By using the idea of clear path markers, we present a novel algorithm for sorting permutations on an n−broom for n>2 that reduces to a novel 2−broom algorithm and that further reduces to two instances of a 1−broom algorithm. Our single-broom algorithm is similar to that of Kawahara et al.; however, our proof of optimality for the same is simpler.

Efficient estimation of the modified Gromov–Hausdorff distance between unweighted graphs

Gromov–Hausdorff distances measure shape difference between the objects representable as compact metric spaces, e.g. point clouds, manifolds, or graphs. Computing any Gromov–Hausdorff distance is equivalent to solving an NP-hard optimization problem, deeming the notion impractical for applications. In this paper we propose a polynomial algorithm for estimating the so-called modified Gromov–Hausdorff (mGH) distance, a relaxation of the standard Gromov–Hausdorff (GH) distance with similar topological properties. We implement the algorithm for the case of compact metric spaces induced by unweighted graphs as part of Python library scikit-tda, and demonstrate its performance on real-world and synthetic networks. The algorithm finds the mGH distances exactly on most graphs with the scale-free property. We use the computed mGH distances to successfully detect outliers in real-world social and computer networks.

What Needs to be Learned by U.S. Cultural Heritage Professionals? Results from the Digital Preservation Outreach & Education Network

Abstract With the current proliferation of training opportunities available in digital preservation, this study asks: what are the most in demand digital preservation instruction topics? To answer this question, we did a qualitative content analysis of 168 Professional Development Support applications received by the Digital Preservation Outreach and Education Network (DPOE-N) between September 2020 and December 2023. The study finds that the management of digital records and metadata/cataloging standards were the most requested training topics, and that general and broadly applicable skills tend to be the most sought after. This indicates that there is a continuing need to provide education focusing on the core elements of digital preservation and knowledge, and that we have not moved on yet to a place where cultural heritage professionals are solely seeking skills in more advanced or specialized digital preservation topics.

Blockchain and FEF-Based Lightweight Anonymous Authentication Protocol for Wireless Medical Sensor Networks

Authentication ensures the privacy of patients by enabling access control within wireless medical sensor networks. However, many schemes do not consider the resource-constrained environments, making those protocols unusable. Meanwhile, sensitive patient information continues to be stored and accessed in a central location, increasing the risk of “single points of failure.” To solve this problem, the researchers designed a lightweight anonymous authentication protocol based on blockchain technology and fuzzy extraction. First, they created a multiround session key negotiation mechanism. Then, they utilized the fuzzy extraction function to extract and recover multimodal biometric features. Decentralization was achieved through blockchain and smart contracts. Simultaneously, the researchers also provided a formal security proof by Burrows-Abadi-Needham logic. Finally, experiments using the Java Pairing-Based Cryptography Library show that this scheme outperforms the comparison schemes in terms of computational overhead and communication overhead.

Computer Network Design and Applications for West Java Legislative Elections

Legislative elections or Pileg are an annual schedule that is routinely held in Indonesia for five years. The concept of technology 4.0 has a huge opportunity to be applied in the legislative election process in this modern era. Legislative elections that use digital devices connected in such a way have more advantages than legislative elections that use conventional methods. The application of technology 4.0 in legislative elections has advantages in terms of effectiveness, data processing speed, and costs. The design of the legislative election system is made using a network where each polling station will be provided with one server to accommodate votes at the polling station. Then the server synchronizes the data to a central server to calculate the final results obtained. The server is built using the text-based Ubuntu Linux operating system. The application used is web-based, with the programming languages used being HTML, PHP, CSS, and Javascript. The test data shows that the time required for the system to complete each stage of the legislative election process is relatively faster than conventional legislative elections. So it can be concluded that the design of the legislative election system has met the requirements to accelerate the legislative election process. This research uses an SDLC system with a prototype model. Tests are carried out using BlackBox testing to test the suitability of the application with application access.

The Vocational High School Student’s Computational Thinking Skills Comparison between Online and Offline Instructional Process in The Computer Network Subject Context

The Vocational High School Student’s Computational Thinking Skills Comparison between Online and Offline Instructional Process in The Computer Network Subject Context

Phosphoenolpyruvate carboxykinase-2 (PCK2) is a therapeutic target in triple-negative breast cancer.

Metabolic rewiring in malignant transformation is often accompanied by altered expression of metabolic isozymes. Phosphoenolpyruvate carboxykinase-2 (PCK2) catalyzes the rate-limiting step of gluconeogenesis and is the dominant isoform in many cancers including triple-negative breast cancer (TNBC). Our goal was to identify small molecule inhibitors of PCK2 enzyme activity. We assessed the impact of PCK2 down regulation with shRNA on TNBC cell growth in vitro and used AtomNet® deep convolutional neural network software to identify potential small molecule inhibitors of PCK2-based structure. We iteratively tested candidate compounds in an in vitro PCK-2 enzyme assay. The impact of the top hit on metabolic flux and cell viability was also assessed. PCK2 downregulation decreased growth of BT-549 and MDA-MB-231 cells and reduced metabolic flux through pyruvate carboxylase. The first AtomNet® in silico structural screen of 7 million compounds yielded 86 structures that were tested in PCK2 enzyme assay in vitro. The top hit (IC50 = 2.4µM) was used to refine a second round of in silico screen that yielded 82 candidates to be tested in vitro, which resulted in 45 molecules with inhibition > 20%. In the second in vitro screen we also included 3-(3,4-dihydroxyphenyl)-2-hydroxypropanoate, previously suggested to be PCK2 inhibitor based on structure, which emerged as the top hit. The specificity of this compound was tested in PCK1 and PCK2 enzymatic assays and showed IC50 of 500nM and 3.5-27nM for PCK1 and PCK2, respectively. 3-(3,4-dihydroxyphenyl)-2-hydroxypropanoate is a high affinity PCK2 enzyme inhibitor that also has significant growth inhibitory activity in breast cell lines in vitro and represents a potential therapeutic lead compound.

Vilanets: An advanced virtual learning environments to improve higher education students’ learning achievement in computer network course

Vilanets: An advanced virtual learning environments to improve higher education students’ learning achievement in computer network course

Data Exfiltration Anomaly Detection on Enterprise Networks using Deep Packet Inspection

Advanced persistent threats (APT) are threat actors with the advanced Technique, Tactic and Procedure (TTP) to gain covert control of the computer network for a long period of time. These threat actors are the highest cyber attack risk factor for enterprise companies and governments. A successful attack by the APT threat Actors has the capabilities to do physical damage. APT groups are typically state-sponsored and are considered the most effective and skilled cyber attackers. The final goal for the APT Attack is to exfiltrate victims data or sabotage system. This aim of this research is to exercise multiple Machine Learning Approach such as k-Nearest Neighbors and H20 Deep Learning Model and also employ Deep Packet Inspection on enterprise network traffic dataset in order to identify suitable approaches to detect data exfiltration by APT threat Actors. This study shows that combining machine learning techniques with Deep Packet Inspection significantly improves the detection of data exfiltration attempts by Advanced Persistent Threat (APT) actors. The findings suggest that this approach can enhance anomaly detection systems, bolstering the cybersecurity defenses of enterprises. Consequently, the research implications could lead to developing more robust strategies against sophisticated and covert cyber threats posed by APTs.

A Comprehensive Accounting of Construction Materials in Belt and Road Initiative Projects.

The Belt and Road Initiative (BRI) stands as the most ambitious infrastructure project in history, marked by its scale of investment, extensive geographical reach across continents and countries, and a diverse array of projects from roads to digital networks. While the BRI's environmental sustainability has raised concerns, the impacts of construction materials used in these projects have been overlooked, especially in developing countries. Here, we map and account for the materials embodied in the BRI by integrating, for the first time, official governmental project reports, geographical information, and material flow analysis. We pinpoint and analyze the BRI material stocks in each individual project by material types, countries, regions, and sectors. Between 2008 and 2023, 328 million tons of construction materials have accumulated in 540 BRI projects around the world, mostly in Asia and Africa. Aggregates (sand and gravel) constitute the largest share (82%), followed by cement, steel, and other materials. Most of the materials are used in transportation infrastructure. Our work further highlights some limitations in terms of data quality for such sustainability assessments. By shedding light on the significant impact of BRI projects on raw material usage across the globe, this study sets the stage for further investigations into environmental impacts of BRI and material stock-flow-nexus from perspective of an initiative.

The Role of ICT in Maintaining Social Cohesion: Understanding the Potential of Digital Initiatives for Social Networks in Rural Areas☆

AbstractDigital initiatives may have helped to maintain active social networks during restrictive, social distancing measures of the COVID‐19 pandemic. To examine how and under which circumstances digital initiatives can contribute to social cohesion, semistructured interviews with 35 stakeholders of local communities and clubs were conducted. The thematic analysis of the transcribed interviews identified four main themes, characterizing conditions under which digital initiatives successfully contributed to social cohesion. First, preexisting digital routines need to be considered. Information and communications technology (ICT) routines, even if limited, need to be extended for digital initiatives to be successfully integrated into communities. Second, acquiring ICT skills are not a technical but a social problem. Members with limited prior knowledge relied heavily on strong ties to improve their ICT skills to become part of digital networks. Third, social media fatigue is particularly prevalent in those with limited prior ICT experience. Importantly, individual withdrawal from digital networks, resulting from SMF, had a knock‐on effect on others. Finally, communities that were not engaging with ICT dissipated. As such, ICT may contribute to social capital by maintaining social engagement in social networks, particularly if providing additional benefits to the community.

Epidemic modeling for misinformation spread in digital networks through a social intelligence approach

Online digital networks, including social networks, have significantly impacted individuals’ personal and professional lives. Aside from exchanging news and topics of interest, digital networks play an essential role in the diffusion of information, which frequently significantly impacts worldwide societies. In this paper, we present a new mathematical epidemic model for digital networks that considers the sentiment of solitary misinformation in the networks and characteristics of human intelligence that play an important role in judging and spreading misinformation inside the networks. Our mathematical analysis has proved the existence and validity of the system in a real-time environment. Considering the real-world data, our simulation predicts how the misinformation could spread among different global communities and when an intervention mechanism should have to be carried out by the policyholders. Our simulation using the model proves that effective intervention mechanisms by isolating the fake news can effectively control the spread of misinformation among larger populations. The model can analyze the emotional and social intelligence of groups frequently subjected to disinformation and disseminating fake news.

LLM-Twin: mini-giant model-driven beyond 5G digital twin networking framework with semantic secure communication and computation

Beyond 5G networks provide solutions for next-generation communications, especially digital twins networks (DTNs) have gained increasing popularity for bridging physical and digital space. However, current DTNs pose some challenges, especially when applied to scenarios that require efficient and multimodal data processing. Firstly, current DTNs are limited in communication and computational efficiency, since they require to transmit large amounts of raw data collected from physical sensors, as well as to ensure model synchronization through high-frequency computation. Second, current models of DTNs are domain-specific (e.g. E-health), making it difficult to handle DT scenarios with multimodal data processing requirements. Finally, current security schemes for DTNs introduce additional overheads that impair the efficiency. Against the above challenges, we propose a large language model (LLM) empowered DTNs framework, LLM-Twin. First, based on LLM, we propose digital twin semantic networks (DTSNs), which enable more efficient communication and computation. Second, we design a mini-giant model collaboration scheme, which enables efficient deployment of LLM in DTNs and is adapted to handle multimodal data. Then, we designed a native security policy for LLM-twin without compromising efficiency. Numerical experiments and case studies demonstrate the feasibility of LLM-Twin. To our knowledge, this is the first to propose an LLM-based semantic-level DTNs.

Globalization of a telepathology network with artificial intelligence applications in Colombia: The GLORIA program study protocol

In Colombia, cancer is recognized as a high-cost pathology by the national government and the Colombian High-Cost Disease Fund. As of 2020, the situation is most critical for adult cancer patients, particularly those under public healthcare and residing in remote regions of the country. The highest lag time for a diagnosis was observed for cervical cancer (79.13 days), followed by prostate (77.30 days), and breast cancer (70.25 days). Timely and accurate histopathological reporting plays a vital role in the diagnosis of cancer. In recent years, digital pathology has been globally implemented as a technological tool in two main areas: telepathology (TP) and computational pathology. TP has been shown to improve rapid and timely diagnosis in anatomic pathology by facilitating interaction between general laboratories and specialized pathologists worldwide through information and telecommunication technologies. Computational pathology provides diagnostic and prognostic assistance based on histopathological patterns, molecular, and clinical information, aiding pathologists in making more accurate diagnoses. We present the study protocol of the GLORIA digital pathology network, a pioneering initiative, and national grant-approved program aiming to design and pilot a Colombian digital pathology transformation focused on TP and computational pathology, in response to the general needs of pathology laboratories for diagnosing complex malignant tumors. The study protocol describes the design of a TP network to expand oncopathology services across all Colombian regions. It also describes an artificial intelligence proposal for lung cancer, one of Colombia's most prevalent cancers, and a freely accessible national histopathological image database to facilitate image analysis studies.